Safeguarding DevOps Environments: AI-Based Continuous Security Monitoring

Authors

  • Diyyala Sravani Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Vaddeswaram 522502, Andhra Pradesh, India
  • Pilla Sri Viswas Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Vaddeswaram 522502, Andhra Pradesh, India
  • Potru Chandu Kiran Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Vaddeswaram 522502, AndhraPradesh, India
  • Jonnala Rohith Reddy Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Vaddeswaram, 522502, Andhra Pradesh, India
  • N. M. Jyothi Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Vaddeswaram, 522502, Andhra Pradesh, India

Keywords:

DevOps, Security Monitoring, Artificial Intelligence, Continuous Integration, Threat Detection

Abstract

In the ever-evolving landscape of software development, the adoption of DevOps practices has brought unprecedented speed and efficiency to the deployment pipeline.However, the accelerated pace of DevOps can inadvertently expose organizations to increased security risks. To address this challenge, "Safeguarding DevOps Environments: AIBased Continuous Security Monitoring" proposes a novel approach to enhance the security of DevOps environments. This research introduces an advanced, AI-driven system for continuous security monitoring in DevOps workflows. The solution leverages the power of artificial intelligence to proactively detect, analyze, and respond to security threats in real time. By integrating this AI-based system into the DevOps pipeline, organizations can seamlessly safeguard their environments while maintaining the agility and rapid deployment that DevOps offers. This paper explores the technical architecture, the AI models and algorithms employed, and the practical implementation of the continuous security monitoring system. Additionally, it showcases realworld case studies and metrics to highlight the effectiveness of this approach in identifying and mitigating security vulnerabilities and threats across various DevOps environments. The findings demonstrate that integrating AIbased continuous security monitoring into DevOps processes not only fortifies an organization's security posture but also fosters a culture of proactive security awareness. By doing so, this research contributes to a safer and more resilient DevOps ecosystem, empowering organizations to navigate the complex intersection of speed and security in modern software development.

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References

Smeds, J., Nybom, K., and Porres, I. 2015. DevOps: A Definition and Perceived Adoption Impediments, in Proceedings of 16th International Conference on Agile Processes in Software Engineering, and Extreme Programming, Helsinki, Finland, pages 166-177, May 2015.

Turnbull, J. DevOps & Security: 2012. http://www.slideshare.net/jamtur01/security-loves-devopsdevopsdays-austin-2012. Accessed: 2016-01-24.

Bartsch, S. 2011. Practitioners' Perspectives on Security in Agile Development, in Proc. of the 6th International Conference on Availability, Reliability and Security (ARES), Vienna, Austria, pages 479-484, August, 2011.

K. Carter, Francois Raynaud on DevSecOps, in IEEE Software, vol. 34, no. 5, pp. 93-96, 2017. doi: 10.1109/MS.2017.3571578

Sharma, s. and coyne, B. (2015). Devops for dummies, IBM Limited Edition. 2nd ed. New Jersey: John Wiley and Sons, Inc.

OWASP, “OWASP Top 10 - The Ten Most Critical Web Application Security Risks,” 2017.

ForcePoint, (2019, September), What is DevSecOps?, [Online].Available: https://www.forcepoint.com/cyber-edu/devsecops

DevOps, (2018, January), Doug Drinkwater, What is DevSecOps?Developing more secure applications, [Online], Available: https://www.csoonline.com/article/3245748/what-isdevsecopsdeveloping-more-secure-applications.html.

DevOps, (2018, January), Doug Drinkwater, What is DevSecOps?Developing more secure applications, [Online], Available: https://www.csoonline.com/article/3245748/what-isdevsecopsdeveloping-more-secure-applications.html

Jessica Nguyen, Marc Dupuis, “Closing the Feedback Loop Between UX Design, Software Development, Security Engineering, and Operations”, SIGITE '19: Proceedings of the 20th Annual SIG Conference on Information Technology Education.

Sara B. O. Gennari Carturan, Denise Hideko Goya, “A systemsofsystems security framework for requirements definition in cloud environment”, ECSA '19: Proceedings of the 13th European Conference on Software Architecture - Volume 2, ACM, Sep 2019.

Mishra, P., & Srinivas, P. V. V. S. (2021). Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage. IAES International Journal of Artificial Intelligence, 10(4), 889.

Srinivas, P.V.V.S., Mishra, P. (2021). Facial Expression Detection Model of Seven Expression Types Using Hybrid Feature Selection and Deep CNN. In: Bhattacharyya, S., Nayak, J., Prakash, K.B., Naik, B., Abraham, A. (eds) International Conference on Intelligent and Smart Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 1312. Springer, Singapore.

https://doi.org/10.1007/978-981-33-6176-8_10

Srinivas, P.V.V.S., Mishra, P. A novel framework for facial emotion recognition with noisy and de noisy techniques applied in data preprocessing. Int J Syst Assur Eng Manag (2022). https://doi.org/10.1007/s13198-022-01737-8

Dommeti, D., Nallapati, S.R.K., Srinivas, P.V.V.S., Mandhala, V.N.(2023). Repercussions of Incorporating Filters in CNN Model to Boost the Diagnostic Ability of SARS-CoV-2 Virus Using Chest Computed Tomography Scans. In: Ogudo, K.A., Saha, S.K.,Bhattacharyya,

D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558.Springer, Singapore.

https//doi.org/10.1007/978-981-19-6880-8_22.

D. Dommeti, S. R. Nallapati, M. L. Kumar, P. Sampath, A. K and P.V. V. S. Srinivas, "Revolutionizing Fingerprint Forensics: Regeneration and Gender Prediction with Gabor Filters, Otsu's Technique, and Deep Learning," 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 340-347, doi:10.1109/ICAISS58487.2023.10250459

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Published

23.02.2024

How to Cite

Sravani, D. ., Viswas, P. S. ., Kiran, P. C. ., Reddy, J. R. ., & Jyothi, N. M. . (2024). Safeguarding DevOps Environments: AI-Based Continuous Security Monitoring. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 430–438. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4855

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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